Noise Variance Estimation In Wireless Communications For Diversity Combining and Log Likelihood Scaling

ABSTRACT

The present patent application comprises a method and means for demodulating symbols, comprising converting an OFDM symbol from a time domain to a frequency domain, selecting pilot tones, making a soft decision based on received data, and estimating a channel frequency response. In another example, the method and means further comprises selecting guard tones. In another example, the method and means further comprises generating channel estimates for in-band and band-edge pilot tones.

CLAIM OF PRIORITY UNDER 35 U.S.C. §119

The present application for patent is a continuation of, and claims the benefit of priority from, U.S. patent application Ser. No. 11/047,347 entitled “Noise Variance Estimation in Wireless Communications for Diversity Combining and Log-Likelihood Scaling,” filed Jan. 28, 2005, which claims the benefit of priority from U.S. Provisional Patent Application No. 60/611,028, entitled “Noise Variance Estimation for Diversity Combining and Log-likelihood Ratio (LLR) Scaling in Platinum Broadcast” filed Sep. 17, 2004, both of which are assigned to the assignee hereof.

BACKGROUND

1. Field

The present disclosure relates generally to telecommunications, and more specifically, to noise variance estimation techniques in wireless communications.

2. Background

In a typical telecommunications system, the data to be transmitted is encoded with a turbo code, which generates a sequence of symbols, referred to as “code symbols.” Several code symbols may be blocked together and mapped to a point on a signal constellation, thereby generating a sequence of complex “modulation symbols.” This sequence may be applied to a modulator, which generates a continuous time signal, which is transmitted over a wireless channel.

At the receiver, the modulation symbols may not correspond to the exact location of a point in the original signal constellation due to noise and other disturbances in the channel. A demodulator may be used to make soft decisions as to which modulation symbols were most likely transmitted based on the received points in the signal constellation. The soft decisions may be used to extract the Log-Likelihood Ratio (LLR) of the code symbols. The turbo decoder uses the sequence of code symbol LLRs to decode the data originally transmitted.

In a receiver employing multiple antennas, a Pilot Weighted Combining (PWC) technique is often used to combine the soft decisions for each antenna. The combined soft decisions may then be used to compute the LLRs for the code symbols. One problem with this approach is the potential difference in thermal noise for each antenna. As a result, the PWC procedure for combining soft decisions may not optimize the Signal-to-Noise Ratio (SNR). Accordingly, there is a need in the art for an improved demodulation process that considers the thermal noise for one or more antennas mounted on a receiver.

SUMMARY

In view of the above, the described features of the present patent application generally relate to one or more improved systems, methods and/or apparatuses for demodulating symbols.

In one example, the present patent application comprises an OFDM demodulator, comprising a discrete fourier transform configured to convert an OFDM symbol from a time domain to a frequency domain, a pilot tone filter operably connected to the discrete fourier transform, wherein the pilot tone filter is configured to select pilot tones, a data tone filter having a first input operably connected to a first output of the discrete fourier transform and a second input operably connected to a first output of the pilot tone filter, a signal demapper having a first input operably connected to an output of the data tone filter, wherein the data tone filter is configured to pass data from the discrete fourier transform to the signal demapper and the signal demapper is configured make a soft decision based on received data and an estimate of a channel frequency response; and a channel estimator having an input operably connected to a second output of the pilot tone filter.

In another example, the pilot tone filter is also configured to select guard tones.

In another example, the channel estimator is configured to generate channel estimates for in-band and band-edge pilot tones.

In another example, the channel estimator is configured to generate channel estimates for said pilot tones using least-squares channel estimation.

In another example, the channel estimator is configured to provide an estimate of said channel's frequency response.

In another example, the channel estimator comprises an inverse discrete fourier transform configured to convert pilot tones into estimates of a channel impulse response, and a second discrete fourier transform operably connected to the inverse discrete fourier transform.

In another example, the channel estimate is time averaged for said OFDM symbols in a time-slot.

In another example, the second discrete fourier transform is configured to estimate the channel frequency response from the channel impulse response for all tones using interpolation.

In another example, the present patent application comprises a method and means for demodulating symbols, comprising converting an OFDM symbol from a time domain to a frequency domain, selecting pilot tones, making a soft decision based on received data, and estimating a channel frequency response.

In another example, the method and means further comprises selecting guard tones.

In another example, the method and means further comprises generating channel estimates for in-band and band-edge pilot tones.

In another example, the method and means further comprises generating channel estimates for the pilot tones using least-squares channel estimation.

In another example, the method and means further comprises providing an estimate of a channel's frequency response.

In another example, the method and means further comprises converting the pilot tones into estimates of a channel impulse response.

In another example, the method and means further comprises time averaging said channel estimate for the OFDM symbols in a time-slot.

In another example, the method and means further comprises estimating the channel frequency response from the channel impulse response for all tones using interpolation.

It is understood that other embodiments of the present invention will become readily apparent to those skilled in the art from the following detailed description, wherein various embodiments of the invention are shown and described by way of illustration. As will be realized, the invention is capable of other and different embodiments and its several details are capable of modification in various other respects, all without departing from the spirit and scope of the present invention. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual block diagram illustrating an example of a telecommunications system;

FIG. 2 is a conceptual block diagram illustrating an example of a transmitter in communication with a receiver;

FIG. 3 is an example of a transmission waveform for a hybrid multi-access telecommunications system supporting both CDMA and OFDM communications;

FIG. 4 is a conceptual block diagram illustrating the functionality of an OFDM demodulator in a receiver for a hybrid multi-access telecommunications system;

FIG. 5 is a conceptual block diagram illustrating the functionality of an OFDM demodulator in a dual antenna receiver for a hybrid multi-access telecommunications system;

FIG. 6 is a graphical illustration of an OFDM symbol in the frequency domain;

FIG. 7 is a conceptual block diagram illustrating the functionality of a channel estimator capable of computing the effective noise variance for its respective antenna; and

FIG. 8 is a flow diagram illustrating a method of estimating an effective noise variance for the band-edge tones.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appended drawings is intended as a description of various embodiments of the present invention and is not intended to represent the only embodiments in which the present invention may be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details. In some instances, well-known structures and components are shown in block diagram form in order to avoid obscuring the concepts of the present invention.

FIG. 1 is a conceptual block diagram illustrating an example of a telecommunications system. The telecommunications system 100 may include an Access Network (AN) 102 which supports communications between any number of ATs 104. The AN 102 may also be connected to additional networks 110A and 1110B outside the AN 102, such as the Internet, a corporate intranet, a Public Switched Telephone Network (PSTN), a broadcast network, or any other network. The Access Terminal (AT) 104 may be any type of fixed or mobile device that can communicate with the AN 102 including but not limited to a wireless handset or telephone, a cellular telephone, a data transceiver, a paging receiver, a position determination receiver, a modem, or the any other wireless terminal.

The AN 102 may be implemented with any number of base stations dispersed throughout a geographic region. The geographic region may be subdivided into smaller regions known as cells with a base station serving each cell. In high traffic applications, the cell may be further divided into sectors with a base station serving each sector. For simplicity, one Base Station (BS) 106 is shown. A Base Station Controller (BSC) 108 may be used to coordinate the activities of multiple base stations, as well as provide an interface to the networks outside the AN 102.

FIG. 2 is a conceptual block diagram illustrating an example of a transmitter in communication with a receiver. The transmitter 202 and receiver 204 may be stand-alone entities, or integrated into a telecommunications system. In a telecommunications system, the transmitter 202 may be in the base station 106 and the receiver 204 may be in the AT 104. Alternatively, the transmitter 202 may be in the AT 104 and the receiver 204 may be in the base station 106.

At the transmitter 202, a Turbo encoder 206 may be used to apply an iterative coding process to the data to Facilitate Forward Error Correction (FEC). The coding process results in a sequence of code symbols with redundancy used by the receiver 204 to correct errors. The code symbols may be provided to a modulator 208 where they are blocked together and mapped to coordinates on a signal constellation. The coordinates of each point in the signal constellation represents the baseband quadrature components that are used by an analog front end 210 to modulate quadrature carrier signals before transmission over a wireless channel 212.

An analog front end 214 in the receiver 204 may be used to convert the quadrature carrier signals to their baseband components. A demodulator 21 (may translate the baseband components back to their correct points in the signal constellation. Because of noise and other disturbances in the channel 212, the baseband components may not correspond to valid locations in the original signal constellation. The demodulator 216 detects which modulation symbols were most likely transmitted by correcting the received points in the signal constellation by the channel's frequency response, and selecting valid symbols in the signal constellation which are closest to the corrected received points. These selections are referred to as “soft decisions.” Soft decisions are used by LLR computation module 218 to determine the LLR of the code symbols. A turbo decoder 220 uses the sequence of code symbol LLRs to decode the data originally transmitted.

The telecommunications system may be implemented with any number of different technologies. Code Division-Multiple Access (CDMA), which is well known in the art, is just one example. CDMA is a modulation and multiple access schemes based on spread-spectrum communications. In a CDMA telecommunications system, a large number of signals share the same frequency spectrum; as a result, such as system provides high user capacity. This is achieved by transmitting each signal with a different code, and thereby, spreading the spectrum of the signal waveform. The transmitted signals are separated in the receiver by a demodulator that uses a corresponding code to despread the signal. The undesired signals, i.e. signals having a different code, are not despread and contribute to noise.

Orthogonal Frequency Division Multiplexing (OFDM) is another example of a technology that can be implemented by a telecommunications system. OFDM is a spread-spectrum technique wherein data is distributed over a large number of carriers spaced apart at precise frequencies. The spacing provides “orthogonality” to prevent a receiver from seeing frequencies other than those intended for the receiver. OFDM, which is also well known in the art, is commonly used for commercial and private broadcasts, but is not limited to such applications.

In at least one embodiment of the telecommunications system, a hybrid multi-access scheme may be employed using both CDMA and OFDM communications. This hybrid system has been gaining widespread acceptance in the area of broadcast services integrated into existing infrastructures, wherein such infrastructures were originally designed to support point-to-point communications between a transmitter and receiver. In other words, the one-to-one type communication system is also being used for one-to-many broadcast transmissions by use of OFDM modulation in combination with other technologies. In these systems, the transmitter may be used to puncture OFDM symbols into a CDMA waveform.

FIG. 3 is an example of a transmission waveform for a hybrid multi-access telecommunications system supporting both CDMA and OFDM communications. The structure of the transmission waveform, as well as the specified time durations, chip lengths, and value ranges are provided by way of example with the understanding that other time durations, chip lengths, and value ranges may be used without departing, from the underlying principles of operation of the telecommunications system. The term “chip” is referred to herein as a unit of time of a binary digit output by a spread-spectrum code generator. This example is consistent with a system supporting the protocol “cdma2000 High Rate Packet Data Air Interface Specification,” TIA/EIA/IS-856.

The transmission waveform 300 may be defined in terms of frames. A frame may include 16 time-slots 302, each time-slot 302 corresponding to 2048 chips. Time slot 302 having a 1.66 millisecond (ins) time-slot duration, and consequently, a 26.66 ms frame duration. Each time-slot 302 may be divided into two half-time-slots 302A, 302B, with CDMA pilot tone bursts 304A, 304B transmitted within each half-time-slot 302A, 302B, respectively. Each CDMA pilot tone burst 304A, 304B may be 96 chips, centered about a mid-point of its associated half-time-slot 302A, 302B. A Medium Access Control (MAC) channel 306A, 306B, 306C, 306D may comprise two bursts, which are transmitted immediately before and immediately after the pilot tone burst 304A, 304B of each half-time-slot 302A, 302B. The MAC may include up to 64 spread-spectrum code channels, which are orthogonally covered by 64-ary Walsh codes. The MAC channels may be used for CDMA overhead such as power control, data rate control, and the like. Data may be sent in the remaining portions 308A, 308B of the first half-time-slot 302A and the remaining portions 308C, 308D of the second half-time-slot 302B.

In one embodiment of a hybrid telecommunications system, four OFDM symbols may be punctured into the data portion of a time-slot 302. This results in a zeroth OFDM symbol 308A at the beginning of the first half-time-slot 302A, a first OFDM symbol 308B at the end of the first half-time-slot 302A, a second OFDM 308C symbol at the beginning of the second half-time-slot 302B, and a third OFDM symbol at the end of the second half-time-slot 302B. In this example, each OFDM symbol is 400 chips. A cyclic prefix 310 occupies 80 chips, leaving 320 chips to transmit data and pilot tones. The 320 chips translates into 320 equally spaced orthogonal tones across the frequency band. Because the tones at the edges of the frequency band may be affected by Adjacent Channel Interference (ACI), the skilled artisan may chose not to send any data on those tones. Instead, the edges of the frequency band, referred to as “guard-bands,” may be used to transmit “pilot tones” and “guard tones.” The tones that are not affected by ACI are typically used to transmit modulation symbols with interspersed pilot tones. Both the guard tones and pilot tones are modulated with known data. Depending on the application, the guard tones and the pilot tones may be the same or different.

FIG. 4 is a conceptual block diagram illustrating the functionality of an OFDM demodulator in a receiver for a hybrid multi-access telecommunications system. The OFDM demodulator 402 may be integrated into any processing entity, or distributed among any number of processing entities, within the receiver. The processing entity (or entities) may include a microprocessor, Digital Signal Processor (DSP), or any other hardware and/or software based processing entity (or entities). Alternatively, the OFDM demodulator 402 may be a separate processing entity such as a microprocessor, DSP, programmable logic, dedicated hardware, or any other entity capable of processing information.

The OFDM demodulator 402 may include a Discrete Fourier Transform (DFT) 404, which may be used to process OFDM symbols. The DFT 402 may be used to convert an OFDM symbol from the time domain to the frequency domain. The output of the DFT 404 may be provided to a pilot tone filter 406 in a serial fashion. The pilot tone filter 406 may be implemented as a decimator to select the pilot tones. The decimator may also be configured to select all guard tones. Signaling from the pilot tone filter 406 to a data tone filter 407 may be used to indicate when the data tone filter 407 should pass data from the DFT 404 to a signal demapper 410. The signal demapper 410 makes a soft decision as to the modulation symbol in the signal constellation most likely transmitted on the data tone. This decision is based on received data and an estimate of the channel's frequency response provided by a channel estimator 408. The channel estimator 408 may estimate the channel's frequency response from the pilot tones using a least-squares channel estimation procedure, or any other suitable procedure.

The channel estimator 408 may be implemented with an Inverse Discrete Fourier Transform (IDFT) 412. The IDFT 412 converts the pilot tones from the frequency domain into the estimate of the channel impulse response of length P samples in the time domain, where P is the number of pilot tones in the OFDM symbol. The channel's frequency response may then be estimated for all tones from the estimate of the channel impulse response using an interpolation process implemented by a DFT 414. The number of samples used by the DFT 414 to compute the channel estimate may be reduced if the actual channel's impulse response is less than PT, where l/T is equal to the chip rate of the OFDM symbol. In this case, the channel's frequency response may be estimated from L samples, where LT is equal to the time duration of the channel's impulse response. The term L is generally referred to as the “delay spread” of the channel impulse response.

The channel estimate may be improved by time-averaging the channel estimates for all OFDM symbols in any given time-slot. In the example discussed in FIG. 3, four channel estimates from four ODFM symbols may be time-averaged. Ideally, a non-causal symmetric filter should be used to time-average the channel estimates for the four ODFM symbols. By way of example, the channel estimate for the first OFDM symbol 308B may be computed by averaging the channel estimates for the zeroth, first, and second OFDM symbols 308A, 308B, 308C. Similarly, the channel estimate for the second OFDM symbol 308C may be computed by averaging the first, second, and third OFDM symbols 308B, 308C, 308D. This approach minimizes the channel estimation bias caused by channel variation induced by Doppler. However, for the zeroth and third OFDM symbols 308A, 308D, this is not possible because adjacent time-slots may contain CDMA signals. Hence, a non-causal filter cannot be applied to the zeroth and third OFDM symbols 308A, 308D. Instead, the channel estimate for the zeroth OFDM symbol 308A may be computed by a weighted averaging process between the zeroth and first OFDM symbols 308A, 308B, and the channel estimate for the third OFDM symbol 308D may be computed by a weighted averaging process between the second and third OFDM symbols 308C, 308D. Alternatively, the channel estimate for the zeroth OFDM symbol 308A may be computed by a weighted averaging process between the zeroth, first, and second OFDM symbols 308A, 308B, 308C, and the channel estimate for the third OFDM symbol 308D in the time-slot may be computed by a weighted averaging process between the first, second and third OFDM symbols 308B, 308C, 308D. The latter approach, however, may cause significant channel estimation bias at high mobile speeds. Either way, the channel estimates for the first and second OFDM symbols 308B, 308C in the time-slot should be more accurate than the channel estimates for the zeroth and third OFDM symbols 308 a, 308 d in the same time-slot.

In multiple antenna applications employing diversity combining techniques, a sequence of soft decisions may be generated for each antenna. The soft decisions for any given tone (k) may be combined using a Maximum Ratio Combining (MRC) technique before being provided to the LLR computation module. The MRC technique scales each soft decision for a given tone by 1/σ_(eff,k) ^((m)2) for the m^(th) antenna, where the effective noise variance (σ_(eff,k) ^((m)2)) is defined by the following equation:

σ_(eff,k) ^((m)2)=σ_(Δ,k) ^((m)2)+σ_(k) ^((m)2)  (1)

where: σ_(Δ,k) ^((m)2) is the Mean-Square Error (MSE) of the channel estimate for the k-th tone received by the m^(th) antenna; and

σ_(k) ^((m)2) is the noise variance of the k^(th) tone received by the m^(th) antenna.

FIG. 5 is a conceptual block diagram illustrating the functionality of an OFDM demodulator in a dual antenna receiver for a hybrid multi-access telecommunications system. The OFDM demodulator 502 may be implemented in a stand-alone processing entity, distributed among multiple processing entities, or integrated into another receiver entity in the same manner as the OFDM modulator described in connection with FIG. 4. The OFDM demodulator 502 is shown with two demodulating channels 502A, 502B, one for each antenna, but may be implemented with any number of demodulating channels depending on the number of antennas in the receiver. In this example, a noise variance estimator 504A, 504B in each demodulating channel 502A, 502B, respectively, estimates the effective noise variance σ_(eff,k) ^((m)2) for each tone. The soft decisions generated by each signal demapper 410A, 410B are provided to a scaler 506A, 506B, where they are scaled by 1/σ_(eff,k) ^((m)2) before being combined with other scaled soft decisions by an adder 508.

FIG. 6 is a graphical illustration of an OFDM symbol in the frequency domain. As discussed earlier in connection with FIG. 3, each OFDM symbol may include guard-bands 602A, 602B containing only pilot and guard tones. The tones not affected by ACI are typically used to transmit modulation symbols with interspersed pilot tones; however, a tone not affected by ACI, may still have a channel estimate which is affected by ACI. This is because the channel estimate is computed from the interpolation of multiple pilot tones, and in some cases those pilot tones may extend into the guard-band regions. Those tones outside the guard-band regions whose channel estimate are affected by ACI, as well as the tones in the guard-band regions, will be referred to as “band-edge tones.” These tones can be found in the band-edge regions 604A, 604B of the frequency band for the OFDM symbol. The remaining tones, with channel estimates that are not affected by ACI, will be referred to as “in-band tones,” and may be found in the in-band region 606 of the OFDM symbol.

FIG. 7 is a conceptual block diagram illustrating the functionality of a channel estimator capable of computing the effective noise variance for its respective antenna. A noise variance estimator 504 may be used to perform this computation. The noise variance estimator 504 may be implemented as part of the channel estimator 408, may be a stand-alone entity, may be implemented as part of another processing entity within the receiver, or may have its functionality distributed among any number of processing entities in the receiver.

The noise variance estimator 504 may include an in-band estimator 702 configured to compute the effective noise variance for the in-band tones, and a band-edge estimator 704 configured to compute the effective noise variance for the band-edge tones. The output at 702 and the output of 704 are provided to a multiplexer (MUX) 703 or a switch. The output of the MUX 703 is then provided to scaler 506. The effective noise variance for in-band tones may be computed from the in-band pilot tones and the channel estimates for the in-band pilot tones. The effective noise variance for the band-edge tones may be computed from the band-edge pilot tones and the channel estimates for the band-edge pilot tones. The accuracy of the effective noise variance for the band-edge tones may be improved by also using the guard-band tones.

The operation of the in-band estimator will first be discussed. The in-band tones are the tones for which the MSE of the channel estimates are related to the noise variance of the tones by the following equation:

$\begin{matrix} {{\sigma_{\Delta}^{{(m)}2}(n)} \approx {\frac{L}{P}{\sum\limits_{l = 0}^{3}{{c_{n,l}}^{2}\sigma^{{(m)}2}}}}} & (2) \end{matrix}$

where c_(n,l) is the time-averaging weights for the channel estimate of the n^(th) OFDM symbol, and σ^((m)2) is the noise variance of the tones received by the m^(th) antenna, which are not affected by ACI. The tone index, or the subscript k, may be suppressed because the noise variance can be assumed to be the same for all the in-band tones.

Thus, the effective noise variance is related to the noise variance by the following equation:

$\begin{matrix} {{\sigma_{{eff},}^{{(m)}2}(n)} = {\left( {1 + {\frac{L}{P}{\sum\limits_{l = 0}^{3}{c_{n,l}}^{2}}}} \right)\sigma^{{(m)}2}}} & (3) \end{matrix}$

The noise variance σ^((m)2) may be computed and scaled using equation (3) to generate the effective noise variance σ_(eff.) ^((m)2) (n).

A set of in-band pilot tones may be defined as Λ={k; −(P−G)/2≦k≦(P−G)/2}, where G>0 is such that the MSE of the time-averaged channel estimates for the k-th pilot tone can be represented by the following equation:

$\begin{matrix} {{{\sigma_{\Delta,{k\; \delta}}^{{(m)}2}(n)} \approx {\frac{L}{P}{\sum\limits_{l = 0}^{3}{{c_{n,l}}^{2}\sigma^{{(m)}2}}}}},{{{{for}\mspace{14mu} k} \in {\Lambda \mspace{14mu} {and}\mspace{14mu} n}} = 0},1,2,3} & (4) \end{matrix}$

where δ=N/P is the pilot tone spacing, N is the number of orthogonal tones, P is the number of pilot tones, and (G-1) is the number of pilot tones whose channel estimates are affected by ACI.

There are four sets of time-averaging weights for the channel estimation; (c_(0.0), c_(0.1), c_(0.2), c_(0.3)) for the zeroth OFDM symbol, (c_(1.0), c_(1.1), c_(1.2), c_(1.3)) for the first OFDM symbol, (c_(2.0), c_(2.1), c_(2.2), c_(2.3)) for the second OFDM symbol, and (c_(3.0), c_(3.1), c_(3.2), c_(3.3)) for the third OFDM symbol: The estimator for the in-band effective noise variance for the n^(th) OFDM symbol is given by:

$\begin{matrix} {{{{\hat{\sigma}}_{eff}^{{(m)}2}(n)} = {\left( {1 + {\frac{L}{P}{\sum\limits_{j}{c_{n,j}}^{2}}}} \right){\sum\limits_{l = 0}^{3}{w_{l}f_{l}^{(m)}}}}}{f_{l}^{(m)} = {\sum\limits_{k \in \Lambda}{{Y_{k,l}^{(m)} - {\hat{H}}_{k,l}^{(m)}}}^{2}}}} & (5) \end{matrix}$

where:

-   -   w_(l) are the combining weights such that the mean of

$\sum\limits_{l = 0}^{3}{w_{l}f_{l}^{(m)}}$

is σ^((m)2);

-   -   Y_(k,l) ^((m)) is the pilot observation corresponding to the         k^(th) pilot tone of the l^(th) OFDM symbol; and     -   Ĥ_(k,l) ^((m)) is the channel estimate for the k^(th) pilot tone         of the l^(th) OFDM symbol.

An example will be illustrative. In this example, the noise variance estimator will use only the first and second OFDM symbols to estimate the noise variance, which can be represented as follows:

$\begin{matrix} {{{w_{0} = {w_{3} = 0}},{and}}{{w_{l} = {\frac{1}{2\left( {P - G + 1} \right)}\frac{1}{1 - {\frac{2\; L}{P}c_{l,l}} + {\frac{L}{P}{\sum\limits_{j}{c_{l,j}}^{2}}}}}},{{{for}\mspace{14mu} l} = 1},2}} & (6) \end{matrix}$

It can be shown that the mean of |Y_(k,l) ^((m))−Ĥ_(k,l) ^((m)|) ² is

$\left( {1 - {\frac{2\; L}{P}c_{l,l}} + {\frac{L}{P}{\sum\limits_{j}{c_{l,j}}^{2}}}} \right){\sigma^{{(m)}2}.}$

Thus, the mean of f_(l) ^((m)) is

$\left( {P - G + 1} \right)\left( {1 - {\frac{2\; L}{P}c_{l,l}} + {\frac{L}{P}{\sum\limits_{j}{c_{l,j}}^{2}}}} \right)\sigma^{{(m)}2}$

because there are P-G-l in-band pilot tones per OFDM symbol, and using the weights w_(l), the mean of w_(l)f_(l) ^((m)) becomes ½σ^((m)2).

In the example algorithm, only the first and second OFDM symbols are used for the channel estimates. Thus, the mean of the effective noise variance may be represented as follows:

$\begin{matrix} {{\left( {1 + {\frac{L}{P}{\sum\limits_{j}{c_{n,j}}^{2}}}} \right)\left( {{\frac{1}{2}\sigma^{{(m)}2}} + {\frac{1}{2}\sigma^{{(m)}2}}} \right)} = {\left( {1 + {\frac{L}{P}{\sum\limits_{j}{c_{n,j}}^{2}}}} \right)\sigma^{{(m)}2}}} & (7) \end{matrix}$

The weights were designed for the case when the first and second OFDM symbols use the same non-causal symmetric time-averaging weights, i.e. (c_(1.0), c_(1.1), c_(1.2), c_(1.3))=(1/3, 1/3, 1/3, 0), and (c_(2.0), c_(2.1), c_(2.2), c_(2.3))=(0, 1/3, 1/3, 1/3). However, the zeroth and third symbols use different time-averaging filters, e.g., (c_(0.0), c_(0.1), c_(0.2), c_(0.3))=(2/3, 1/3, 0, 0), and (c_(3.0), c_(3.1), c_(3.2), c_(3.3))=(0, 0, 1/3, 2/3). In this case, the time-averaged channel estimates for the zeroth and third OFDM symbols may have large bias due to the time-variation of the channel at high mobile speed. As a result, the condition

${\sigma_{\Delta}^{{(m)}2}(n)} \approx {\frac{L}{P}{\sum\limits_{l = 0}^{3}{{c_{n,l}}^{2}\sigma^{{(m)}2}}}}$

may no longer be satisfied. Hence, the channel estimates for only first and second OFDM symbols are used in this example.

Alternatively, the channel estimate may be based on only one OFDM symbol. In this case, the effective noise variance can be estimated as follows:

$\begin{matrix} {{{\hat{\sigma}}_{eff}^{{(m)}2}(n)} = {\left( {1 + {\frac{L}{P}{\sum\limits_{j}{c_{n,j}}^{2}}}} \right)w_{n,1}f_{1}^{(m)}}} & (8) \\ {with} & \; \\ {w_{n,1} = {\frac{1}{2\left( {P - G + 1} \right)}{\frac{1}{1 - {\frac{2\; L}{P}c_{1,1}} + {\frac{L}{P}{\sum\limits_{j}{c_{1,j}}^{2}}}}.}}} & (9) \end{matrix}$

The mean of the effective noise variance is the same as before. However, the variance is larger than that using two OFDM symbols. In other words, the former is less accurate than the latter.

The accuracy of the effective noise variance may be improved by using the zeroth and third OFDM symbols, but the channel estimates Ĥ_(k,0) ^((m)) and Ĥ_(k,3) ^((m)) used in f₀ ^((m)) and f₃ ^((m)) are without time-averaging, i,e. (c_(0.0), c_(0.1), c_(0.2), c_(0.3))=(1, 0, 0, 0), and (c_(3.0), c_(3.1), c_(3.2), c_(3.3))=(0, 0, 0, 1). In this case, the weights may be represented as follows:

$\begin{matrix} {{w_{l\;} = {\frac{1}{\left( {P - G + 1} \right)}\frac{1}{{2\left( {1 - \frac{L}{P}} \right)} + {\sum\limits_{l = 1}^{2}\left( {1 - {\frac{2L}{P}c_{l,l}} + {\frac{L}{P}{\sum\limits_{j}{c_{l,j}}^{2}}}} \right)}}}},{{{for}\mspace{14mu} l} = 0},1,2,3.} & (10) \end{matrix}$

In sum, the in-band estimator may be used to compute f_(l) ^((m)), or the sum of |Y_(k,l) ^((m))−Ĥ_(k,l) ^((m))|² of in-band tones, to get an estimate of

${\left( {P - G + 1} \right)\left( {1 - {\frac{2L}{P}c_{{l,l}\;}} + {\frac{L}{P}{\sum\limits_{j}{c_{l,j}}^{2}}}} \right)\sigma^{{(m)}2}},$

which is proportional to the noise variance σ^((m)2) of the in-band tones. Combining weights may then be chosen so that the mean of the result is the same as σ_(eff.) ^((m)2)(n) for each OFDM symbol (n=0, 1, 2, 3), i.e., E[{circumflex over (σ)}_(eff) ^((m)2)(n)]=σ_(eff.) ^((m)2)(n). (E[X] denotes expectation or mean of random variable X.) Because different time-averaging weights c_(n,l) for each OFDM symbol,

$\left( {1 + {\frac{L}{P}{\sum\limits_{l = 0}^{3}{c_{n,l}}^{2}}}} \right)$

may also be different for each symbol. This may cause the different tones in different OFDM symbols in the same time-slot to have different effective noise variances. This should be compensated for in the LLR calculation by the term

$\left( {1 + {\frac{L}{P}{\sum\limits_{l = 0}^{3}{C_{n,l}}^{2}}}} \right).$

The band-edge estimator will now be discussed. As discussed earlier, in-band tones and the band-edge tones may be determined by comparing the MSE of the channel estimate for the k-th tone σ_(Δ,k) ^((m)2)(n) with

${\frac{L}{P\;}{\sum\limits_{l = 0}^{3}{{c_{n,l}}^{2}\sigma^{{(m)}2}}}},$

where σ^((m)2) is the noise variance of the tones which are not affected by ACI. If they are close to each other, the tones are classified as in-band tones. Otherwise, they are classified as band-edge tones. Given the delay spread L, the number of pilot tones P, and the characteristics of the ACI, such as the adjacent carrier spacing and its power relative to σ^((m)2), the relation between the MSE of the channel estimate for the k-th tone σ_(Δ,k) ^((m)2)(n) and the noise variance σ^((m)2) may be determined either through mathematical analysis or simulations. This may be done in the system design phase, before the noise variance estimator is integrated into the receiver of an AT, and then stored in memory.

A set of band-edge pilot tones may be represented as Λ={k; (P−G)/2≦k≦P/2−1 or −P/2≦k≦(P−G)/2−1)}, where G−1 is the number of band-edge pilot tones and k is the pilot tone index. This set may be determined in the system design phase, and stored in memory.

An example will be illustrative. In this example, N=320. So the tone index k goes from −160 to 159, (i.e., k=−160, −159, −158, . . . −1, 0, 1 . . . 158, 159). There are 64 pilot tones (i.e., P=64), and 15 band-edge pilot tones (i.e., G=16). The pilot tones are spaced apart by five frequency tones (i.e., tone indices: k=−160, −155, . . . −5, 0, 5, . . . 150, 155.). Based on these conditions, the in-band tones may be represented by −120≦k≦120, the in-band pilot tones may be represented by the pilot tone indices −24≦k≦24, the band-edge tones may be represented by tone indices 121≦k≦159 and −160≦k ≦−121, the band-edge pilot tones may be represented by the pilot tone indices 25≦k≦31 and −32≦k≦−25, and the guard-band tones may be represented by tone indices 150≦k≦159 and −160≦k≦−151. But the pilot tones are punctured in the guard tones, and as a result, the tones at k=−160, −155, 150, 155 are pilot tones. Accordingly, there are K=20−4=16 guard tones. There are 241 in-band tones and 79 band-edge tones. So the majority of the tones are in-band tones. This example also shows the data sent on some band-edge tones.

The noise variance of a band-edge tone is the sum of the noise variance of the in-band tones and the variance of the ACI for the band-edge tones. The MSE of the channel estimate for a band-edge tone is the sum of the MSE of the channel estimate for the in-band tones and the component due to ACI. In general, the amount of the adjacent channel interference in each band-edge tone is different from tone to tone. Hence, it is difficult to accurately estimate the effective noise variances for band-edge tones. However, accounting for the fact that there is ACI in the noise variance estimates, it is possible to improve performance. This is achieved by de-emphasizing the LLR's computed from the tones affected by ACI.

The noise variance estimator may be designed such that, in the absence of ACI, the following equation is satisfied:

${E\left\{ {{\hat{\sigma}}_{{eff},k}^{{(m)}2}(n)} \right\}} = {\left( {1 + {\frac{L}{P}{\sum\limits_{l = 0}^{3}{c_{n,l}}^{2}}}} \right){\sigma^{{(m)}2}.}}$

In other words, if there is no ACI, the mean of the noise variance estimator should be the same as the effective noise variance.

The noise variance estimator may be used to account for the increase in the effective noise variance due to the ACI. This may be achieved by using the band-edge tones and guard tones to estimate the effective noise variance. The mean of the estimate will not be the same as the effective noise variance. However, it will be larger than the in-band noise variance estimate. Hence, the band-edge tones will be de-emphasized in LLR computation.

The following band-edge estimator algorithm may be used. The algorithm may be used to compute the average of the variances of the band-edge tones as follows:

$\begin{matrix} {{{{\overset{\_}{\sigma}}_{aci}^{{(m)}2}(n)} = {\left( {1 + {\frac{L}{P}{\sum\limits_{j}{c_{n,j}}^{2}}}} \right)\left( {{\overset{3}{\sum\limits_{l = 0}}{\lambda_{l}g_{l}^{(m)}}} + {\sum\limits_{l = 0}^{3}{\mu_{l}q_{l}^{(m)}}}} \right)}}{g_{l\;}^{(m)} = {\sum\limits_{k \in \overset{\_}{\Lambda}}{{Y_{k,l}^{(m)} - {\hat{H}}_{k,l}^{(m)}}}^{2}}}{q_{l}^{(m)} = {\sum\limits_{k \in \Omega}{X_{k,l}^{(m)}}^{2}}}} & (12) \end{matrix}$

where:

Ω is a set of tone indices for the guard tones;

X_(k,l) ^((m)) is the k-th tone for the l-th OFDM symbol:

Y_(k,l) ^((m))=X_(kδ,l) ^((m)), where δ=N/P is a pilot tone spacing. By way of example, the first pilot tone is the fifth tone of the OFDM symbol, so Y_(1,l) ^((m))=X_(5,l) ^((m));

λ_(l) and μ_(l) are the combining weights.

K is the number of elements in the set Ω i.e., the number of guard tones within an OFDM symbol. If there are sixteen guard tones, by way of example, K is 16. λ_(l) and μ_(l) are chosen in the system design phase so that in the absence of ACI, the following relationship exists:

$\begin{matrix} {\sigma^{{(m)}2} = {E\left\{ {{\sum\limits_{l = 0}^{3}{\lambda_{l}g_{l}^{(m)}}} + {\sum\limits_{l = 0}^{3}{\mu_{l}q_{l}^{(m)}}}} \right\}}} & (13) \end{matrix}$

where E[X] denotes expectation or mean of a random variable X. In the absence of ACI, this condition makes the mean of σ _(aci) ^((m)2)(n) the same as the effective noise variance. In the presence of ACI, this σ _(aci) ^((m)2)(n) may be used as an estimate of the effective noise variance for the “edge tones” (i.e., k=((N/2)−1) and k=−(N/2)). Note that the edge tone at k=((N/2)−1) is the rightmost tone in the positive frequency, and the edge tone at k=−(N/2) is the leftmost tone in the negative frequency.

In the absence of ACI, the effective noise variance of the band-edge should be the same as the in-band effective noise variance. But in the presence of ACI, the band-edge effective noise variance will be larger than the in-band effective noise variance. Since the noise variance estimates are noisy, it may be possible that σ _(aci) ^((m)2)(n)<{circumflex over (σ)}_(eff) ^((m)2)(n), which is not correct. Thus, the term σ _(aci) ^((m)2)(n) can be refined as follows. The maximum of σ _(aci) ^((m)2)(n) and the estimate for the in-band tones are taken to be the variances for the edge tones:

$\begin{matrix} {{{\hat{\sigma}}_{{eff},{{N/2} - 1}}^{{(m)}2}(n)} = {{{\hat{\sigma}}_{{eff},{{- N}/2}}^{{(m)}2}(n)} = {\max \left\{ {{{\overset{\_}{\sigma}}_{aci}^{{(m)}2}(n)},{{\hat{\sigma}}_{eff}^{{(m)}2}(n)}} \right\}}}} & (14) \end{matrix}$

where {circumflex over (σ)}_(eff) ^((m)2)(n) is the estimate of the in-band effective noise variance for the n^(th) OFDM symbol.

Next, the effective noise variance for the other band-edge tones, {circumflex over (σ)}_(eff,k) ^((m)2)(n) are interpolated between in-band variance {circumflex over (σ)}_(eff) ^((m)2)(n) and {circumflex over (σ)}_(eff,N/2) ^((m)2)(n).

In the absence of ACI, the mean of |Y_(k,l) ^((m))−Ĥ_(k,l) ^((m))|² is

$\left( {1 - {\frac{2L}{P}c_{l,l}} + {\frac{L}{P}{\sum\limits_{j}{c_{l,j}}^{2\;}}}} \right){\sigma^{{(m)}2}.}$

Thus, the mean of g_(l) ^((m)) is

${\left( {G - 1} \right)\left( {1 - {\frac{2L}{P}c_{l,l}} + {\frac{L}{P}{\sum\limits_{j}{c_{l,j}}^{2}}}} \right)\sigma^{{(m)}2}},$

since there are (G−1) elements in the sum. The mean of |X_(k,l) ^((m))|² is σ^((m)2), since there is no signal in the guard tones. Accordingly, the mean of q_(l) ^((m)) is given by the following equation:

$\begin{matrix} {{{E\left\{ q_{l}^{(m)} \right\}} = {{E\left\{ {\sum\limits_{k \in \Omega}{X_{k,l}^{(m)}}^{2}} \right\}} = {K\; \sigma^{{(m)}\; 2}}}};} & (15) \end{matrix}$

where K is the number of guard tones. Thus, by properly choosing λ_(l) and μ_(l), one can make the mean of

${\sum\limits_{l = 0}^{3}{\lambda_{l}g_{l}^{(m)}}} + {\sum\limits_{l = 0}^{3}{\mu_{l}q_{l}^{(m)}}}$

the same as σ^((m)2).

In the presence of ACI, g_(l) ^((m)) and q_(l) ^((m)) will contain the ACI contributions. So the σ _(aci) ^((m)2)(n) becomes larger than σ^((m)2) to account for the increased effective noise variance. This is used in LLR computation to de-emphasize the LLRs from the band-edge tones.

Another example may be illustrative.

λ₀=λ₃=0,

$\begin{matrix} {{{\lambda_{l\;} = {\frac{1}{2\left( {G + K - 1} \right)}\frac{1}{1 - {\frac{2L}{P}c_{l,l}} + {\frac{L}{P}{\sum\limits_{j}{c_{l,j}}^{2}}}}}},{{{for}\mspace{14mu} l} = 1},2}{{\mu_{0} = {\mu_{3} = 0}},{and}}} & (16) \\ {{\mu_{l} = \frac{1}{2\left( {G + K - 1} \right)}},{{{for}\mspace{14mu} l} = 1},2,} & (17) \end{matrix}$

where K is the number of guard-band tones within an OFDM symbol, i.e. the number of elements in the set Ω and G−1 is the number of band-edge pilot tones.

If only guard tones are used:

$\begin{matrix} {{\lambda_{0} = {\lambda_{1} = {\lambda_{2} = {\lambda_{3\;} = 0}}}},{and}} & (18) \\ {\mu_{0} = {\mu_{1} = {\mu_{2} = {\mu_{3} = \frac{1}{4K}}}}} & (19) \end{matrix}$

FIG. 8 is a flow diagram illustrating a method of estimating an effective noise variance for the band-edge tones according to one example. An average effective noise variance is estimated for the band-edge tones from the band-edge pilot tones, the channel estimates for the band-edge pilot tones, and the guard tones 802. The edge tones of the OFDM symbol are assigned to an effective noise variance equal to the maximum of the average effective noise variance for the band-edge tones and the effective noise variance for the in-band tones 804. The effective noise variance for the band-edge tones are interpolated between the effective noise variance of the edge tones and the effective noise variance of the iii-band tones 806.

In the various embodiments of the receiver, methods for computing the effective noise variances of OFDM symbols are described for multiple antenna applications using diversity techniques. However, the method described throughout this disclosure for computing noise variances of OFDM symbols may have many applications. By way of example, the noise variances of OFDM symbols may be useful for SNR estimations. LLR computations, and other processing functions. In multiple antenna applications. noise variance computations may be used to determine which antenna is more reliable. It can also be used to dc-emphasize the less reliable data and emphasize the more reliable data received in OFDM symbols when computing LLRs. Those skilled in the alt will readily appreciate that there may be numerous applications in which noise variance information may be useful.

The various illustrative logical blocks, modules, circuits, elements, and/or components described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic component, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing components, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The methods or algorithms described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, Read only Memory (ROM), Electrically Programmable ROM (EPROM) memory, Electrically Erasable Programmable ROM (EEPROM) memory, registers, hard disk, a removable disk, a Compact Disc ROM (CD-ROM) or any other form of storage medium known in the art. A storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. 

1. A demodulator, comprising; a discrete fourier transform configured to convert a symbol from a time domain to a frequency domain; a pilot tone filter operably connected to said discrete fourier transform, wherein said pilot tone filter is configured to select pilot tones; a data tone filter having a first input operably connected to a first output of said discrete fourier transform and a second input operably connected to a first output of said pilot tone filter; a signal demapper having a first input operably connected to an output of said data tone filter, wherein said data tone filter is configured to pass data from said discrete fourier transform to said signal demapper and said signal demapper is configured make a soft decision based on received data and an estimate of a channel frequency response; and a channel estimator having an input operably connected to a second output of said pilot tone filter.
 2. The demodulator according to claim 1, wherein said pilot tone filter is also configured to select guard tones.
 3. The demodulator according to claim 1, wherein said channel estimator is configured to generate channel estimates for in-band and band-edge said pilot tones.
 4. The demodulator according to claim 1, wherein said channel estimator is configured to generate channel estimates for said pilot tones using least-squares channel estimation.
 5. The demodulator according to claim 1, wherein said channel estimator is configured to provide an estimate of said channel's frequency response.
 6. The demodulator according to claim 1, wherein said channel estimator comprises: an inverse discrete fourier transform configured to convert pilot tones into estimates of a channel impulse response; and a second discrete fourier transform operably connected to said inverse discrete fourier transform.
 7. The demodulator according to claim 3, wherein said channel estimator comprises: an inverse discrete fourier transform configured to convert pilot tones into estimates of a channel impulse response; and a second discrete fourier transform operably connected to said inverse discrete fourier transform.
 8. The demodulator according to claim 5, wherein said symbols are OFDM symbols and wherein said channel estimate is time averaged for said OFDM symbols in a time-slot.
 9. The demodulator according to claim 6, wherein said second discrete fourier transform is configured to estimate said channel frequency response from said channel impulse response for all tones using interpolation.
 10. The demodulator according to claim 7, wherein said symbols are OFDM symbols, wherein said channel estimate is time averaged for said OFDM symbols in a time-slot and wherein said second discrete fourier transform is configured to estimate said channel frequency response from said channel impulse response for tones using interpolation.
 11. A method of demodulating symbols, comprising; converting a symbol from a time domain to a frequency domain; selecting pilot tones; making a soft decision based on received data; and estimating a channel frequency response.
 12. The method of demodulating according to claim 1, further comprising selecting guard tones.
 13. The method of demodulating symbols according to claim 1, further comprising generating channel estimates for in-band and band-edge said pilot tones.
 14. The method of demodulating symbols according to claim 11, further comprising generating channel estimates for said pilot tones using least-squares channel estimation.
 15. The method of demodulating symbols according to claim 11, further comprising providing an estimate of a channel's frequency response.
 16. The method of demodulating symbols according to claim 11, further comprising converting said pilot tones into estimates of a channel impulse response.
 17. The method of demodulating symbols according to claim 13, further comprising converting said pilot tones into estimates of a channel impulse response.
 18. The method of demodulating symbols according to claim 15, wherein said symbols are OFDM symbols and further comprising time averaging said channel estimate for said OFDM symbols in a time-slot.
 19. The method of demodulating symbols according to claim 16, further comprising estimating said channel frequency response from said channel impulse response for all tones using interpolation.
 20. The method of demodulating symbols according to claim 17, wherein said symbols are OFDM symbols and further comprising time averaging said channel estimate for said OFDM symbols in a time-slot; and estimating said channel frequency response from said channel impulse response for tones using interpolation.
 21. An apparatus for demodulating symbols, comprising; means for converting a symbol from a time domain to a frequency domain; means for selecting pilot tones; means for making a soft decision based on received data; and means for estimating a channel frequency response.
 22. The apparatus for demodulating according to claim 21, further comprising means for selecting guard tones.
 23. The apparatus for demodulating symbols according to claim 21, further comprising means for generating channel estimates for in-band and band-edge said pilot tones.
 24. The apparatus for demodulating symbols according to claim 21, further comprising means for generating channel estimates for said pilot tones using least-squares channel estimation.
 25. The apparatus for demodulating symbols according to claim 21, further comprising means for providing an estimate of a channel's frequency response.
 26. The apparatus for demodulating symbols according to claim 21, further comprising means for converting said pilot tones into estimates of a channel impulse response.
 27. The apparatus for demodulating symbols according to claim 23, further comprising means for converting said pilot tones into estimates of a channel impulse response.
 28. The apparatus for demodulating symbols according to claim 25, wherein said symbols are OFDM symbols and further comprising means for time averaging said channel estimate for said OFDM symbols in a time-slot.
 29. The apparatus for demodulating symbols according to claim 26 further comprising means for estimating said channel frequency response from said channel impulse response for all tones using interpolation.
 30. The apparatus for demodulating symbols according to claim 27, wherein said symbols are OFDM symbols and further comprising means for time averaging said channel estimate for said OFDM symbols in a time-slot; and means for estimating said channel frequency response from said channel impulse response for tones using interpolation.
 31. A computer program product, comprising: computer-readable medium comprising: code for causing a computer to demodulate symbols comprising convert a symbol from a time domain to a frequency domain; select pilot tones; make a soft decision based on received data; and estimate a channel frequency response.
 32. The computer program product according to claim 31, further comprising code to select guard tones.
 33. The computer program product according to claim 31, further comprising code to generate channel estimates for in-band and band-edge said pilot tones.
 34. The computer program product according to claim 31, further comprising code to generate channel estimates for said pilot tones using least-squares channel estimation.
 35. The computer program product according to claim 31, further comprising code to provide an estimate of a channel's frequency response.
 36. The computer program product according to claim 31, further comprising code to convert said pilot tones into estimates of a channel impulse response.
 37. The computer program product according to claim 33, further comprising code to convert said pilot tones into estimates of a channel impulse response.
 38. The computer program product according to claim 35, wherein said symbols are OFDM symbols and further comprising code to time average said channel estimate for said OFDM symbols in a time-slot.
 39. The computer program product according to claim 36, further comprising code to estimate said channel frequency response from said channel impulse response for all tones using interpolation.
 40. The computer program product according to claim 37, wherein said symbols are OFDM symbols and further comprising code to time average said channel estimate for said OFDM symbols in a time-slot; and estimate said channel frequency response from said channel impulse response for tones using interpolation. 